{"title":"调查合并抑郁症和焦虑症的桥接症状之间的网络结构和因果关系:贝叶斯网络分析","authors":"Yu Wang, Zhongquan Li, Xing Cao","doi":"10.1002/jclp.23663","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.</p>\n </section>\n </div>","PeriodicalId":15395,"journal":{"name":"Journal of Clinical Psychology","volume":"80 6","pages":"1271-1285"},"PeriodicalIF":2.5000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Investigating the network structure and causal relationships among bridge symptoms of comorbid depression and anxiety: A Bayesian network analysis\",\"authors\":\"Yu Wang, Zhongquan Li, Xing Cao\",\"doi\":\"10.1002/jclp.23663\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Method</h3>\\n \\n <p>Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusion</h3>\\n \\n <p>BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.</p>\\n </section>\\n </div>\",\"PeriodicalId\":15395,\"journal\":{\"name\":\"Journal of Clinical Psychology\",\"volume\":\"80 6\",\"pages\":\"1271-1285\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Clinical Psychology\",\"FirstCategoryId\":\"102\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/jclp.23663\",\"RegionNum\":3,\"RegionCategory\":\"心理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PSYCHOLOGY, CLINICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Clinical Psychology","FirstCategoryId":"102","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jclp.23663","RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PSYCHOLOGY, CLINICAL","Score":null,"Total":0}
Investigating the network structure and causal relationships among bridge symptoms of comorbid depression and anxiety: A Bayesian network analysis
Background
The network analysis method emphasizes the interaction between individual symptoms to identify shared or bridging symptoms between depression and anxiety to understand comorbidity. However, the network analysis and community detection approach have limitations in identifying causal relationships among symptoms. This study aims to address this gap by applying Bayesian network (BN) analysis to investigate potential causal relationships.
Method
Data were collected from a sample of newly enrolled college students. The network structure of depression and anxiety was estimated using the Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder (GAD-7) Scale measures, respectively. Shared symptoms between depression and anxiety were identified through network analysis and clique percolation (CP) method. The causal relationships among symptoms were estimated using BN.
Results
The strongest bridge symptoms, as indicated by bridge strength, include sad mood (PHQ2), motor (PHQ8), suicide (PHQ9), restlessness (GAD5), and irritability (GAD6). These bridge symptoms formed a distinct community using the CP algorithm. Sad mood (PHQ2) played an activating role, influencing other symptoms. Meanwhile, restlessness (GAD5) played a mediating role with reciprocal influences on both anxiety and depression symptoms. Motor (PHQ8), suicide (PHQ9), and irritability (GAD6) assumed recipient positions.
Conclusion
BN analysis presents a valuable approach for investigating the complex interplay between symptoms in the context of comorbid depression and anxiety. It identifies two activating symptoms (i.e., sadness and worry), which serve to underscore the fundamental differences between these two disorders. Additionally, psychomotor symptoms and suicidal ideations are recognized as recipient roles, being influenced by other symptoms within the network.
期刊介绍:
Founded in 1945, the Journal of Clinical Psychology is a peer-reviewed forum devoted to research, assessment, and practice. Published eight times a year, the Journal includes research studies; articles on contemporary professional issues, single case research; brief reports (including dissertations in brief); notes from the field; and news and notes. In addition to papers on psychopathology, psychodiagnostics, and the psychotherapeutic process, the journal welcomes articles focusing on psychotherapy effectiveness research, psychological assessment and treatment matching, clinical outcomes, clinical health psychology, and behavioral medicine.